In a world filled with opinions about the impact of artificial intelligence (AI), it’s worth having your own view of the big picture of this latest technological revolution.
By Juanita Vorster, independent business advisor
My view is that we shouldn’t turn the conversation into a comparison. It’s not about who does it better, or who’s going to win or lose.
For tradespeople – or artisans, as they’re called in South Africa – the conversation should focus on the collaboration between human and artificial intelligence through all the stages needed to fulfil the overarching purpose of artisan work: creating things that make life better, safer, easier, or more enjoyable.
Collaboration in training
Chatbot-based AI tools like ChatGPT use large language models (LLMs) trained on information from a huge range of fields. During an artisan’s training phase, these tools can act as lightning-fast research assistants, offering existing knowledge relevant to a specific question – packaged in a way that’s quick and easy to understand.
But the strength of these tools is also their limitation: because they rely on existing information, they can’t create anything original. That’s where human trainers are still essential. Their creativity and ability to adapt to different learners’ needs are things AI can’t replicate.
Human trainers also support more than just learning the theory – and since trades are by definition about applying knowledge through hands-on experience, there’s a lot of value in combining what people and machines each do best. The potential for positive collaboration in artisan training is huge.
Collaboration in design
In the design phase, humans bring creative thinking, while AI tools can offer memory, idea comparisons, storage and recall, and even theory-based testing of early designs.
If artisans learn how to use these tools properly, they can spend more time focusing on what they’re trained to do. For those with more experience, it’s also a chance to keep pushing their skills and exploring new ways of doing things.
Collaboration in production
Looking at the robotics side of AI, there’s also plenty of space for working together. A robot – which still needs to be programmed by someone who knows the job – can repeat tasks without getting tired. But it still can’t recognise when something’s gone wrong.
This means artisans may take on more of a supervisory or robot-training role, rather than doing the task directly. But even that might not be the case everywhere – or any time soon. Technology may move fast, but it often takes much longer for large-scale, cost-effective rollout to happen.
Collaboration in process optimisation and quality control
Improving processes and maintaining quality takes loads of data, plus the know-how to spot where small tweaks can make a difference. AI can help by capturing, storing, analysing and comparing that data, which can save time, effort, and money.
Even so, it’s still a collaboration – not a handover. Machines look at data, but they can’t take human behaviour or preferences into account. That’s why artisans are still key to making sure that everything runs in a way that works for people, not just numbers.
Human artisans are still needed to use their special skill – being human – to ensure that processes and quality are optimised for the human experience.